Spatial variability of air pollutants in a megacity characterized by mobile measurements: Chemical homogeneity under haze conditions

Khuzestani, Reza Bashiri; Liao, Keren; Chen, Qi; Liu, Ying; Zheng, Yan; Cheng, Xi; Jia, Tianjiao; Li, Xin; Chen, Shiyi; Huang, Guancong

doi:https://doi.org/10.5194/acp-2021-651

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https://doi.org/10.5194/acp-2021-651

Preprints

24 Aug 2021

Status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Spatial variability of air pollutants in a megacity characterized by mobile measurements: Chemical homogeneity under haze conditions

Reza Bashiri Khuzestani^1,a,, Keren Liao^1,, Qi Chen¹, Ying Liu¹, Yan Zheng¹, Xi Cheng¹, Tianjiao Jia¹, Xin Li¹, Shiyi Chen¹, and Guancong Huang¹ Reza Bashiri Khuzestani et al.

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¹State Key Joint Laboratory of Environmental Simulation and Pollution Control, BIC-ESAT and IJRC, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
^anow at: Faculty of Civil, Water and Environmental Engineering, School of Engineering, Shahid Beheshti University, Tehran, Iran
These authors contributed equally to this work.

Received: 03 Aug 2021 – Discussion started: 24 Aug 2021

Abstract. Characterization of the spatial distributions of air pollutants on an intracity scale is important for understanding localized sources, secondary formation, and human exposure. In this study, we conducted in situ mobile measurements for the chemical composition of fine particles, volatile organic compounds (VOCs), oxygenated VOCs (OVOCs), and common gas pollutants in winter in the megacity of Beijing. The spatial variations of these gaseous and particulate pollutants under different pollution conditions are investigated. During the less-polluted periods, a large spatial variability exists in the inorganic composition of fine particles, suggesting a wide range of particle neutralization in Beijing. Significant spatial variations are also observed in the composition of organic aerosol (OA), which is mainly driven by local emissions of primary OA from vehicle and cooking exhaust. The spatial variations of VOCs and OVOCs vary by species. In general, hydrocarbon compounds show a large spatial variability driven by traffic emissions, while secondary OVOCs are more spatially homogeneous in concentration. Other gas pollutants show relatively low spatial variabilities, although hot spots of concentration frequently appear which are plausibly caused by high-emitting plumes as well as fast on-road ozone titration. During the haze periods, the spatial variabilities of air pollutants are largely reduced because of the contribution of regional transport. Hydrocarbons and less-oxygenated OVOCs show good positive spatial-temporal correlations in concentration. More-oxygenated OVOCs show good positive correlations among themselves and moderate negative correlations with hydrocarbons, less-oxygenated OVOCs, and particulate components. The results highlight the potential role of chemical homogeneity on the SOA production in the megacity under haze conditions. On the other hand, the spatial heterogeneity of air pollution calls a future need of using fine-resolution models to evaluate human exposure and pollution control strategies.

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Reza Bashiri Khuzestani et al.

Status: closed

RC1:
'Comment on acp-2021-651', Anonymous Referee #3, 11 Sep 2021

The paper reported the on-road mobile measurement results in megacity in China. It is interesting that homogeneous and heterogeneous spatial distributions were observed respectively for haze and clean days. The fine spatial resolution measurement provided a lot of information on localized sources, which is potentially useful for the development of future pollution control strategies. Overall, the paper is well written and logically organized. High-spatial resolution measurements is important yet scarce in China. As one of the pioneering studies in China, I recommend the paper be published subject to minor revision.

Specific comments:

1. Line 85, both mass resolution and time resolution of the ToF-ACSM sampling should be provided.

2. Line 94 and Line 100, I don’t think this is a good way to describe how the PMF results were derived and how the instruments were run during the campaign. Although experimental details had been published in the papers from the same group, readers may not have read the other ones and it is not their duty to do so. As an independent submission, at least all the necessary experimental details should be provided in SI to aid understanding of the whole manuscript.

3. Line 125: The authors run the mobile lab on the highway, which is largely affected by the on-road vehicle emissions. Although self-contamination from the exhaust of the mobile lab could be eliminated, I’m not sure whether the data could represent the characteristic the specific area as shown on each pie in Figure 1. In another word, if the mobile lab was run on the road several meters away from the highway, would similar composition distributions be derived?

4. Lines 125-145, it is interesting that on clean days great spatial variability of aerosol components was observed. What about the daily variation? I’m curious whether the observed spatial variation can well represent the local emission. Also, why the authors specifically present the results of the noon cycles instead of the average of the whole cycles for one day or during all clean days’ sampling since the campaign lasted for around 2 weeks.

5. Line 164: megacity scale? Or the authors meant the regional scale?

6. Line 248: Why hydrocarbons accumulated in the afternoon (12:00pm-14:00pm)? Hydrocarbons should decrease during the noon time because of photochemical consumption as observed from on-site measurements in literature.

7. From the discussion in Section 3.3, it seems variations of VOCs and OVOCs species are predominantly driven by on-road vehicles or high-emitting plumes. The running cycles on the 4^th Ring Road cover different regions characterized by different functions, such as industrial area, residential area, etc., yet the VOC characteristics in different regions were not discussed in detail except vehicle emission. Could more information on local sources for different regions be derived from the measurements? After all, mobile emission is not the only emission source.

8. Line 540: Legend, non-haze and haze days should be denoted in Figure 4.

Citation: https://doi.org/10.5194/acp-2021-651-RC1
- AC2: 'Reply on RC1', Qi Chen, 11 Oct 2021
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-651/acp-2021-651-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2021-651-AC2
RC2:
'Comment on acp-2021-651', Anonymous Referee #1, 21 Sep 2021

This manuscript describes mobile measurements of PM mass and composition, inorganic gases, and organic vapors on haze and non-haze days in Beijing. I like the study design, which focuses on quantifying the broad spatial patterns by repeatedly driving a ring road. This is in contrast to many previous mobile sampling studies that focused on obtaining neighborhood-level details at high spatial resolution.

However, I have some major criticisms that need to be addressed:

(1) Amount and representativeness of data: The analyses (all figures except Fig 4) rely on only two days of data (November 14 and 18, 2018). Additionally, the authors primarily discuss midday concentrations on those days. For example, Fig 1 show data from the midday drives between 11:00 am and 12:30 pm local time. Since each drive takes around 70 minutes, this means that the majority of the analysis focuses on one or two drives on each of two days.

The authors claim some large conclusions (they imply that their results are representative of all haze days and all clean days). They therefore need to show more than a small slice of data on two days. The results they present here are for two days, and therefore not necessarily representative of broader conditions in Beijing. A revised version of the manuscript should include analysis from multiple clean and haze days to get a better sense of how robust the results are.

While writing this review I looked up a 2018 calendar. November 14 was a Wednesday, and November 18 was a Sunday. I am unfamiliar with the typical Chinese workweek or people's activity patterns in Beijing, but it seems like there is a good chance that most of this paper's analysis compares a single working day to a single non-working day.

(2) Interpretation of spatial homogeneity on the haze day: The authors need to provide readers with a better sense of meteorological conditions on the clean versus haze days, and how those conditions relate to their interpretation of the mobile measurements. My assumption is that the haze days have low wind speed and perhaps a low mixing height, whereas the non-haze days are windier and better mixed. That seems to be the case from the data shown in Figure S3, but the authors need to include some of that context in the manuscript.

Since the haze day has lower wind speed and presumably poorer mixing, I would expect significant spatial variability, especially for primary emissions. I might even expect larger spatial gradients on haze than non-haze days because of poor dispersion. Instead, the authors explain the more homogeneous conditions on the haze day as a result of "regional transport." That doesn't make sense to me as an explanation, since the haze day seems to be a case of stagnant air where local emissions are trapped.

The local emissions seem to be significant. Figure 4 shows that there are strong enough local emissions on the clean day to replenish pollutant concentrations after the boundary layer rises in the morning (e.g., hydrocarbon concentrations are higher from 12-14 and 14-16 than from 10-12). Thus, if emissions were similar on the two days, one would expect a larger daytime increase in concentrations, not a flat profile. If the haze day was a non-working day (Sunday, see comment above), emissions would be very different, and would have a major impact on the temporal patterns.

(3) With the exception of Figure 4, the authors do not show any temporal variations. I would expect that there is a lot to learn from comparing spatial patterns at different times of day (e.g., morning rush versus midday). Not showing this data in more detail seems like a major missed opportunity.

Additional comments:

(1) Figure 1a and 1d show the spatial variation of PM1 concentrations on two days. This figure is supposed to show that there is more variability on the clean day, however that is not obvious given the scaling of the symbols. The two days both look homogenous to me.

(2) Lines 129-130 note that most of the OA spatial variability on the clean day is due to variations in POA. However, the CV for OOA mass concentration (0.76) is similar to the CV for HOA (.79). This suggests that OOA is also variable. Though, as the authors note, I would expect OOA to be more spatially homogeneous. Perhaps this high CV for OOA points to some misapportionment of other OA types as OOA.

(2) Fig 4 - Make it clean which panels are haze versus clear days. I assume that grey shading is for the haze days.

(4) Fig 4 - how many days are in each plot? Please be clear about how much data is being shown.

Citation: https://doi.org/10.5194/acp-2021-651-RC2
- AC1: 'Reply on RC2', Qi Chen, 11 Oct 2021
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-651/acp-2021-651-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2021-651-AC1

Status: closed

RC1:
'Comment on acp-2021-651', Anonymous Referee #3, 11 Sep 2021

The paper reported the on-road mobile measurement results in megacity in China. It is interesting that homogeneous and heterogeneous spatial distributions were observed respectively for haze and clean days. The fine spatial resolution measurement provided a lot of information on localized sources, which is potentially useful for the development of future pollution control strategies. Overall, the paper is well written and logically organized. High-spatial resolution measurements is important yet scarce in China. As one of the pioneering studies in China, I recommend the paper be published subject to minor revision.

Specific comments:

1. Line 85, both mass resolution and time resolution of the ToF-ACSM sampling should be provided.

2. Line 94 and Line 100, I don’t think this is a good way to describe how the PMF results were derived and how the instruments were run during the campaign. Although experimental details had been published in the papers from the same group, readers may not have read the other ones and it is not their duty to do so. As an independent submission, at least all the necessary experimental details should be provided in SI to aid understanding of the whole manuscript.

3. Line 125: The authors run the mobile lab on the highway, which is largely affected by the on-road vehicle emissions. Although self-contamination from the exhaust of the mobile lab could be eliminated, I’m not sure whether the data could represent the characteristic the specific area as shown on each pie in Figure 1. In another word, if the mobile lab was run on the road several meters away from the highway, would similar composition distributions be derived?

4. Lines 125-145, it is interesting that on clean days great spatial variability of aerosol components was observed. What about the daily variation? I’m curious whether the observed spatial variation can well represent the local emission. Also, why the authors specifically present the results of the noon cycles instead of the average of the whole cycles for one day or during all clean days’ sampling since the campaign lasted for around 2 weeks.

5. Line 164: megacity scale? Or the authors meant the regional scale?

6. Line 248: Why hydrocarbons accumulated in the afternoon (12:00pm-14:00pm)? Hydrocarbons should decrease during the noon time because of photochemical consumption as observed from on-site measurements in literature.

7. From the discussion in Section 3.3, it seems variations of VOCs and OVOCs species are predominantly driven by on-road vehicles or high-emitting plumes. The running cycles on the 4^th Ring Road cover different regions characterized by different functions, such as industrial area, residential area, etc., yet the VOC characteristics in different regions were not discussed in detail except vehicle emission. Could more information on local sources for different regions be derived from the measurements? After all, mobile emission is not the only emission source.

8. Line 540: Legend, non-haze and haze days should be denoted in Figure 4.

Citation: https://doi.org/10.5194/acp-2021-651-RC1
- AC2: 'Reply on RC1', Qi Chen, 11 Oct 2021
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-651/acp-2021-651-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2021-651-AC2
RC2:
'Comment on acp-2021-651', Anonymous Referee #1, 21 Sep 2021

This manuscript describes mobile measurements of PM mass and composition, inorganic gases, and organic vapors on haze and non-haze days in Beijing. I like the study design, which focuses on quantifying the broad spatial patterns by repeatedly driving a ring road. This is in contrast to many previous mobile sampling studies that focused on obtaining neighborhood-level details at high spatial resolution.

However, I have some major criticisms that need to be addressed:

(1) Amount and representativeness of data: The analyses (all figures except Fig 4) rely on only two days of data (November 14 and 18, 2018). Additionally, the authors primarily discuss midday concentrations on those days. For example, Fig 1 show data from the midday drives between 11:00 am and 12:30 pm local time. Since each drive takes around 70 minutes, this means that the majority of the analysis focuses on one or two drives on each of two days.

The authors claim some large conclusions (they imply that their results are representative of all haze days and all clean days). They therefore need to show more than a small slice of data on two days. The results they present here are for two days, and therefore not necessarily representative of broader conditions in Beijing. A revised version of the manuscript should include analysis from multiple clean and haze days to get a better sense of how robust the results are.

While writing this review I looked up a 2018 calendar. November 14 was a Wednesday, and November 18 was a Sunday. I am unfamiliar with the typical Chinese workweek or people's activity patterns in Beijing, but it seems like there is a good chance that most of this paper's analysis compares a single working day to a single non-working day.

(2) Interpretation of spatial homogeneity on the haze day: The authors need to provide readers with a better sense of meteorological conditions on the clean versus haze days, and how those conditions relate to their interpretation of the mobile measurements. My assumption is that the haze days have low wind speed and perhaps a low mixing height, whereas the non-haze days are windier and better mixed. That seems to be the case from the data shown in Figure S3, but the authors need to include some of that context in the manuscript.

Since the haze day has lower wind speed and presumably poorer mixing, I would expect significant spatial variability, especially for primary emissions. I might even expect larger spatial gradients on haze than non-haze days because of poor dispersion. Instead, the authors explain the more homogeneous conditions on the haze day as a result of "regional transport." That doesn't make sense to me as an explanation, since the haze day seems to be a case of stagnant air where local emissions are trapped.

The local emissions seem to be significant. Figure 4 shows that there are strong enough local emissions on the clean day to replenish pollutant concentrations after the boundary layer rises in the morning (e.g., hydrocarbon concentrations are higher from 12-14 and 14-16 than from 10-12). Thus, if emissions were similar on the two days, one would expect a larger daytime increase in concentrations, not a flat profile. If the haze day was a non-working day (Sunday, see comment above), emissions would be very different, and would have a major impact on the temporal patterns.

(3) With the exception of Figure 4, the authors do not show any temporal variations. I would expect that there is a lot to learn from comparing spatial patterns at different times of day (e.g., morning rush versus midday). Not showing this data in more detail seems like a major missed opportunity.

Additional comments:

(1) Figure 1a and 1d show the spatial variation of PM1 concentrations on two days. This figure is supposed to show that there is more variability on the clean day, however that is not obvious given the scaling of the symbols. The two days both look homogenous to me.

(2) Lines 129-130 note that most of the OA spatial variability on the clean day is due to variations in POA. However, the CV for OOA mass concentration (0.76) is similar to the CV for HOA (.79). This suggests that OOA is also variable. Though, as the authors note, I would expect OOA to be more spatially homogeneous. Perhaps this high CV for OOA points to some misapportionment of other OA types as OOA.

(2) Fig 4 - Make it clean which panels are haze versus clear days. I assume that grey shading is for the haze days.

(4) Fig 4 - how many days are in each plot? Please be clear about how much data is being shown.

Citation: https://doi.org/10.5194/acp-2021-651-RC2
- AC1: 'Reply on RC2', Qi Chen, 11 Oct 2021
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-651/acp-2021-651-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2021-651-AC1

Reza Bashiri Khuzestani et al.

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Short summary

This work characterized the spatial variabilities of air pollutants in a megacity by advanced mobile measurements. The results show a large spatial heterogeneity in the distributions of PM_2.5 composition and VOCs under clean conditions, and rather uniform spatial distributions under haze conditions that may indicate a chemical homogeneity on an intracity scale. The findings improve our understanding of urban air pollution and human exposure.


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